Deep neural networks, in particular deep convolutional neural networks (CNNs) [1]}, [2]}, [3]} became a key tool in medical imaging analysis
[4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. In particular, deep neural networks have showed excellent performance in 2D medical imaging data such as the interpretation of X-ray scans [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [15]}. However, training very deep networks on high-resolution 3D volumes of Computed Tomography (CT) scans requires a huge computing resource. For examples, each typical 3D CT scans study may contain from hundreds to thousands slices, which makes training deep neural networks on this modality challenging in terms of computing requirements (see Figure REF ). In this study, we aim to develop an efficient 2D deep learning-based approach for 3D medical imaging analysis that is able to provide the same level of performance compared to 3D-based learning approaches. To tackle this challenge, we consider each 3D CT scan as a set of 2D images and propose a new strategy to train slice-level classifiers on CT based on the descriptors of the adjacent slices along the axis, each of which is extracted through a convolutional neural network (CNN). Extracted features are then concatenated to produce better representations for classification tasks. This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset [22]}, which aims to predict the presence of ICH and classify it into 5 different sub-types.
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